Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Uses Managed Agents' 14.5-hour runtime to audit an entire codebase overnight. Security, performance, accessibility, dependency issues. You wake up to a full report.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Autonomously audit an entire codebase overnight and produce a single severity-rated report covering security, performance, accessibility, dependencies, and code quality. Built for Anthropic's Managed Agents runtime (14.5-hour task horizon): run to completion without questions or confirmation, writing structured findings to disk as execution proceeds so partial results survive interruption.
references/reconnaissance.md -- Phase 1 steps and the reconnaissance report template.references/shared-rubric.md -- Severity rating rubric and structured finding format. Pass both to every agent.references/agent-security.md -- Security Auditor brief and output format.references/agent-performance.md -- Performance Auditor brief and output format.references/agent-accessibility.md -- Accessibility Auditor brief, skip condition, and output format.references/agent-dependency.md -- Dependency Auditor brief, skip condition, and output format.references/agent-code-quality.md -- Code Quality Auditor brief and output format.references/compilation-and-templates.md -- Phase 3 compilation steps, final report template, and completion message.audit-workspace/ directory and overnight-audit-report.md. Never modify, build, or execute project code. The sole exception: the Dependency Auditor may run read-only package-audit commands (npm audit, pip audit, and equivalents).audit-workspace/00-reconnaissance.md as the shared context document for all agents.Follow references/reconnaissance.md for exact commands and the report template.
run_in_background: true on every call and send ALL agent calls in a single message.references/shared-rubric.md, and the agent-specific brief from its reference file. Each agent writes to its own output file under audit-workspace/.
references/agent-security.md -> 01-security-audit.mdreferences/agent-performance.md -> 02-performance-audit.mdreferences/agent-accessibility.md -> 03-accessibility-audit.md (honor skip condition)references/agent-dependency.md -> 04-dependency-audit.md (honor skip condition)references/agent-code-quality.md -> 05-code-quality-audit.mdovernight-audit-report.md to the repository root, then emit the brief completion message.Follow references/compilation-and-templates.md for the deduplication map, final report template, and completion message.
| Codebase Size | Estimated Duration | Agent Strategy |
|---|---|---|
| < 10K lines | 15-30 minutes | All agents, single pass each |
| 10K - 50K lines | 30-90 minutes | All agents, thorough pass |
| 50K - 200K lines | 1-4 hours | All agents, may need sub-agents for Security and Code Quality |
| 200K - 500K lines | 4-8 hours | All agents spawn 2-3 sub-agents each to parallelize file review |
| 500K+ lines | 8-14 hours | Full sub-agent deployment with file-batch assignments per sub-agent |
For codebases over 200K lines, each audit agent should spawn sub-agents to parallelize within its domain (for example, Security splits into Auth & Sessions, Data Handling, API Surface, and Infrastructure). Each brief instructs agents to self-organize sub-agent deployment based on the size discovered during their audit.
audit-workspace/ directory and overnight-audit-report.md. Suggest the user commit or copy prior results first..gitignore exists but does not list audit-workspace/, suggest adding it to prevent committing audit output.还没有评价。欢迎分享你的使用感受。
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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